[Emotional labour of nursing care: an evolutionary concept analysis].
Bibliographic record
Abstract
UNLABELLED: Caring is considered as the essence of nursing. Underpinning caring, the internal regulation of emotions or the emotional labour of nurses is invisible. The concept of emotional labour is relatively underdeveloped in nursing. DATA SOURCES: A literature search using keywords 'emotional labour', 'emotional work' and 'emotions' was performed in CINAHL, psycINFO and REPERE from 1990 to January 2008. We analysed 72 papers whose main focus of inquiry was on emotional labour. REVIEW METHODS: We followed Rodgers' evolutionary method of concept analysis. RESULTS: Emotional labour is a process whereby nurse adopt a 'work persona' to express their autonomous, surface or deep emotions during patient encounters. Antecedents to this adoption of a work persona are events occurring during patient-nurse encounters, and which consist of three elements : organization (i.e.social norms, social support), nurse (i.e.role identification, professional commitment, work experience and interpersonal skills) and job (i.e.autonomy, task routine, degree of emotional demand, interaction frequency and work complexity). The attributes of emotional labour have two dimensions : nurses' autonomous response and their work persona strategies (i.e. surface or deep acts). The consequences of emotional labour include organizational (i.e.productivity, 'cheerful environment') and nurse aspects (i.e. negative or positive) CONCLUSION: the concept of emotional labour should be introduced into preregistration programmes. Nurses also need to have time and a supportive environment to reflect, understand and discuss their emotional labour in caring for 'difficult' patients to deflate the dominant discourse about 'problem' patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.025 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".